Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning

DOI: 10.4236/jcc.2015.311006   PDF   HTML   XML   2,227 Downloads   2,658 Views   Citations


In this paper, we proposed a new semi-supervised multi-manifold learning method, called semi- supervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturally gives relative importance to the labeled ones through a graph-based methodology. Then it tries to extract discriminative features on each manifold such that the data points in the same manifold become closer. The effectiveness of the proposed multi-manifold learning algorithm is demonstrated and compared through experiments on a real hyperspectral images.

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Huang, H. , Luo, F. , Ma, Z. and Feng, H. (2015) Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning. Journal of Computer and Communications, 3, 33-39. doi: 10.4236/jcc.2015.311006.

Conflicts of Interest

The authors declare no conflicts of interest.


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